Applied Sciences,
Journal Year:
2022,
Volume and Issue:
12(3), P. 1491 - 1491
Published: Jan. 29, 2022
In
recent
years,
the
application
of
artificial
intelligence
has
been
revolutionizing
manufacturing
industry,
becoming
one
key
pillars
what
called
Industry
4.0.
this
context,
we
focus
on
job
shop
scheduling
problem
(JSP),
which
aims
at
productions
orders
to
be
carried
out,
but
considering
reduction
energy
consumption
as
a
objective
fulfill.
Finding
best
combination
machines
and
jobs
performed
is
not
trivial
becomes
even
more
involved
when
several
objectives
are
taken
into
account.
Among
them,
improvement
savings
may
conflict
with
other
objectives,
such
minimization
makespan.
paper,
provide
an
in-depth
review
existing
literature
multi-objective
optimization
metaheuristics,
in
consumption.
We
systematically
reviewed
critically
analyzed
most
relevant
features
both
formulations
algorithms
solve
them
effectively.
The
manuscript
also
informs
empirical
results
main
findings
our
bibliographic
critique
performance
comparison
among
representative
evolutionary
solvers
applied
diversity
synthetic
test
instances.
ultimate
goal
article
carry
out
critical
analysis,
finding
good
practices
opportunities
for
further
that
stem
from
current
knowledge
vibrant
research
area.
IEEE Transactions on Evolutionary Computation,
Journal Year:
2021,
Volume and Issue:
26(3), P. 461 - 475
Published: Aug. 19, 2021
With
increasing
environmental
awareness
and
energy
requirement,
sustainable
manufacturing
has
attracted
growing
attention.
Meanwhile,
distributed
systems
have
become
emerging
due
to
the
development
of
globalization.
This
article
addresses
energy-aware
hybrid
flow-shop
scheduling
(EADHFSP)
with
minimization
makespan
consumption
simultaneously.
We
present
a
mixed-integer
linear
programming
model
propose
cooperative
memetic
algorithm
(CMA)
reinforcement
learning
(RL)-based
policy
agent.
First,
an
encoding
scheme
reasonable
decoding
method
are
designed,
considering
tradeoff
between
two
conflicting
objectives.
Second,
problem-specific
heuristics
presented
for
initialization
generate
diverse
solutions.
Third,
solutions
refined
appropriate
improvement
operator
selected
by
RL-based
effective
solution
selection
based
on
decomposition
strategy
is
utilized
balance
convergence
diversity.
Fourth,
intensification
search
multiple
operators
incorporated
further
enhance
exploitation
capability.
Moreover,
energy-saving
strategies
designed
improving
nondominated
The
effect
parameter
setting
investigated
extensive
numerical
tests
carried
out.
comparative
results
demonstrate
that
special
designs
CMA
superior
existing
algorithms
in
solving
EADHFSP.
European Journal of Operational Research,
Journal Year:
2022,
Volume and Issue:
309(1), P. 1 - 23
Published: Aug. 11, 2022
In
industry,
production
is
often
organized
in
the
form
of
a
hybrid
flow
shop,
and
there
great
interest
methods
algorithms
for
optimizing
such
processes.
While,
thus
far,
have
focused
mostly
on
single
selected
objective,
it
increasingly
important
to
address
several
objectives
simultaneously
order
move
from
extreme
balanced
solutions
that
consider
diverse
operational
requirements.
Following
this,
we
classify
characterize
literature
dealing
with
multi-objective
shop
scheduling
problems
(HFSP).
We
identify
those
features
metaheuristics
require
particular
attention
during
process
finding
Pareto
HFSP
(especially
coding
decoding
schemes,
archives,
dominance
concepts).
To
promote
evaluation
suitability
solving
multi-criteria
HFSP,
provide
an
overview
test
instances
used
propose
systematization
performance
criteria
fronts
create
clear
consistent
conceptual
semantic
understanding.
Based
recommendations
are
derived
can
also
be
helpful
various
optimization
other
application
contexts
assessing
solution
quality
as
accurately
comparably
possible.
Finally,
current
challenges
possible
future
research
directions
highlighted.
Journal of Industrial Information Integration,
Journal Year:
2024,
Volume and Issue:
39, P. 100598 - 100598
Published: March 12, 2024
Recent
advancements
in
production
scheduling
have
arisen
response
to
the
need
for
adaptation
dynamic
environments.
This
paper
addresses
challenge
of
real-time
within
context
sustainable
production.
We
redefine
distributed
permutation
flow-shop
problem
using
an
online
mixed-integer
programming
model.
The
proposed
model
prioritizes
minimizing
makespan
while
simultaneously
constraining
energy
consumption,
reducing
number
lost
working
days
and
increasing
job
opportunities
permissible
limits.
Our
approach
considers
machines
operating
different
modes,
ranging
from
manual
automatic,
employs
two
strategies:
predictive-reactive
proactive-reactive
scheduling.
evaluate
rescheduling
policies:
continuous
event-driven.
To
demonstrate
model's
applicability,
we
present
a
case
study
auto
workpiece
manage
complexity
through
various
reformulations
heuristics,
such
as
Lagrangian
relaxation
Benders
decomposition
initial
optimization
well
four
problem-specific
heuristics
considerations.
For
solving
large-scale
instances,
employ
simulated
annealing
tabu
search
metaheuristic
algorithms.
findings
underscore
benefits
strategy
efficiency
event-driven
policy.
By
addressing
challenges
integrating
sustainability
criteria,
this
contributes
valuable
insights
into
IEEE Transactions on Cybernetics,
Journal Year:
2020,
Volume and Issue:
51(3), P. 1390 - 1402
Published: Feb. 11, 2020
This
article
presents
a
surrogate-assisted
multiswarm
optimization
(SAMSO)
algorithm
for
high-dimensional
computationally
expensive
problems.
The
proposed
includes
two
swarms:
the
first
one
uses
learner
phase
of
teaching-learning-based
(TLBO)
to
enhance
exploration
and
second
particle
swarm
(PSO)
faster
convergence.
These
swarms
can
learn
from
each
other.
A
dynamic
size
adjustment
scheme
is
control
evolutionary
progress.
Two
coordinate
systems
are
used
generate
promising
positions
PSO
in
order
further
its
search
efficiency
on
different
function
landscapes.
Moreover,
novel
prescreening
criterion
select
individuals
exact
evaluations.
Several
commonly
benchmark
functions
with
their
dimensions
varying
30
200
adopted
evaluate
algorithm.
experimental
results
demonstrate
superiority
over
three
state-of-the-art
algorithms.
International Journal of Production Research,
Journal Year:
2020,
Volume and Issue:
59(13), P. 3880 - 3899
Published: May 20, 2020
With
the
development
of
global
and
decentralised
economies,
distributed
production
emerges
in
large
manufacturing
firms.
A
model
exists
with
hybrid
flowshops.
As
an
extension
flowshop
scheduling
problem
(HFSP),
(DHFSP)
sequence
dependent
setup
times
(SDST)
is
a
new
challenging
project.
The
DHFSP
involves
three
sub-problems:
first
one
to
allocate
factory
for
each
job;
second
determine
job
factory;
third
machine
at
stage.
This
paper
presents
position-based
mathematical
discrete
artificial
bee
colony
algorithm
(DABC)
DHFSP-SDST
optimise
makespan.
proposed
DABC
employs
two-level
encoding
ensure
initiative
scheduling.
Decoding
method
combines
earliest
available
completion
time
rule
feasible
schedules.
also
employ
effective
solutions
update
techniques:
neighbourhood
operators,
many
Critical
Factory
Swap
enhance
exploitation.
780
benchmarks
total
are
generated.
Extensive
experiments
carried
out
test
performance
DABC.
Computational
results
statistical
analyses
validate
that
outperforms
best
performing
literature.
